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This study investigates the relationships between perceived scarcity, fear of missing out (FOMO) and impulse-buying tendencies (IBT) in the fast fashion context in both scarcity and non-scarcity conditions. Additionally, this study examines whether these relationships vary depending on the type of scarcity messages: limited-quantity scarcity (LQS) and limited-time scarcity (LTS).

We used written scenarios, and each participant was assigned to one of the experimental or control groups for LQS and LTS conditions. Using a structural modeling approach, we tested the conceptual model and analyzed the data through SmartPLS version 4. We conducted mediating and multigroup (MGA) analysis.

We found that perceived scarcity directly increases IBT and that FOMO partially mediates this relationship across all samples. The MGA findings also revealed that hypothesized relationships were not significantly different across LQS and LTS groups, suggesting that the effect of scarcity messages may be context specific.

Previous studies have yielded mixed results on the effects of different scarcity messages on consumer behavior. This study contributes to the literature by providing evidence of the direct relationship between perceived scarcity, FOMO and impulse buying in the fast fashion context. The study supports the idea that the effect of different types of scarcity messages is context specific, suggesting that the relationship between scarcity perceptions and consumer behavior may vary depending on the product category and cultural context.

This study aims to investigate whether organizational complexity (hereafter firm complexity) increases audit report lag (ARL) in a unique environment of GCC countries.

The research study uses a panel data set of 6,084 firm-year observations of nonfinancial firms from GCC economies from 2009 to 2022. First, the study uses an ordinary least square estimator to examine the association of firm complexity with ARL. Second, for robustness purposes, the study applies the propensity score matching technique.

This research study finds that the firms’ complexity increases ARL. Supporting the argument that auditors respond to firm complexity with increased effort, the authors find a positive relation of firm complexity with ARL. This relationship is augmented by auditor change, auditors’ tenure, auditor-qualified opinion and adoption of IFRS. In addition, the authors also find that Big-4 and audit firm industry specialization curtail the positive impact of firm complexity on ARL.

Firms in the GCC have less time to complete their audit and complex firms are likelier to have bigger ARLs. This study provided evidence regarding the curtailing effect of audit quality in GCC. Our findings suggest policymakers and reformers choose improved audit quality to reduce the possibility of larger ARL.

This study enriches the scholarship by presenting a mechanism for reducing the ARL of complex firms through higher audit quality. This study contributes to agency theory by emphasizing audit quality’s important role in emerging markets.

The focus is on determining the long-term relationship in explaining how technological capabilities interact with trade and global value chain (GVC) participation to aid in the upgradation process using a panel auto-regressive distributed lag (ARDL) model. The results suggest that export of both low-skill and medium-skill technology-intensive manufactures and patents by residents positively and significantly impact GVC participation.

This paper examines the dynamic linkages between GVC participation and technological capability of major Asian countries in a comparative (1995–2018) perspective.

This implies that certain sectors enable greater integration into GVCs in the long-run, supported by critical learning variables. Further, with the help of the panel causality test, a bi-directional flow between GVC participation and export of high-technology manufactures and import of labour-intensive technology manufactures is witnessed. Even a one-way flow from research and development (R&D) intensity to GVC participation is seen.

The technological capabilities are found to be characterising the initial structure of local enterprises in trade and GVCs, as well as the extent to which emerging-market firms may harness knowledge flows and migrate into high-tech industries.

The purpose of the study is to investigate how small- and medium-sized enterprises (SMEs) can effectively collaborate for eco-innovation using the business modes of innovation framework to emphasise three types of collaboration: “science, technology, and innovation” (STI), “learning by doing, using, and interacting” (DUI)-Vertical and DUI-Horizontal.

This analysis uses data from 838 SMEs in the Basque Country (2018–2020) to evaluate the effects of the three types of collaboration on eco-innovation. The authors employ a propensity score-based method to address potential bias associated with endogeneity in innovation studies.

The findings suggest that DUI-Vertical collaboration has a positive relationship with the development of product, process and marketing eco-innovation. Furthermore, DUI-horizontal collaboration is the most effective collaboration mode for SMEs, positively impacting their overall eco-innovation portfolio. Finally, STI collaboration is positively associated with product eco-innovation.

Policymakers should support SMEs by designing programmes that facilitate collaboration between competing firms to stimulate eco-innovation, but potential challenges of coopetition must be addressed. Rather than a generic, one-size-fit-all approach, SMEs' managers should identify the most appropriate partners corresponding to their specific eco-innovation goal, ensuring a more effective and targeted. Collaboration between science partners and SMEs should be reinforced by approximating the SMEs' needs more effectively.

This study contributes twofold. Firstly, the authors investigate whether the STI and DUI modes of innovation are determinant factors in the introduction of various types of eco-innovation. Secondly, the authors contribute to the literature on business modes of innovation by differentiating between DUI-Vertical (i.e. suppliers, customers and consultancy) and DUI-Horizontal (i.e. competitors) collaboration, thus highlighting the complexity of DUI collaboration forms.

In this paper, the authors investigate the impact of the confirmation bias on returns, expectations and hedging of optimistic and pessimistic traders in the cryptocurrencies, commodities and stock markets before and during COVID-19 periods.

The authors investigate the impact of the confirmation bias on the estimated returns and the expectations of optimistic and pessimistic traders by employing the financial stochastic model with confirmation bias. Indeed, the authors compute the optimal portfolio weights, the optimal hedge ratios and the hedging effectiveness.

The authors find that without confirmation bias, during the two sub periods, the expectations of optimistic and pessimistic trader’s seem to convergence toward zero. However, when confirmation bias is particularly strong, the average distance between these two expectations are farer. The authors further show that, with and without confirmation bias, the optimal weights (the optimal hedge ratios) are found to be lower (higher) for all pairs of financial market during the COVID-19 period as compared to the pre-COVID-19 period. The authors also document that the stronger the confirmation bias is, the lower the optimal weight and the higher the optimal hedge ratio. Moreover, results reveal that the values of the optimal hedge ratio for optimistic and pessimistic traders affected or not by the confirmation bias are higher during the COVID-19 period compared to the estimates for the pre-COVID period and inversely for the optimal hedge ratios and the hedging effectiveness index. Indeed, either for optimists or pessimists, the presence of confirmation bias leads to higher optimal hedge ratio, higher optimal weights and higher hedging effectiveness index.

The findings of the study provided additional evidence for investors, portfolio managers and financial analysts to exploit confirmation bias to make an optimal portfolio allocation especially during COVID-19 and non-COVID-19 periods. Moreover, the findings of this study might be useful for investors as they help them to make successful investment decision in potential hedging strategies.

First, this is the first scientific work that conducts a stochastic analysis about the impact of emotional biases on the estimated returns and the expectations of optimists and pessimists in cryptocurrency and commodity markets. Second, the originality of this study stems from the fact that the authors make a comparative analysis of hedging behavior across different markets and different periods with and without the impact of confirmation bias. Third, this paper pays attention to the impact of confirmation bias on the expectations and hedging behavior in cryptocurrencies and commodities markets in extremely stressful periods such as the recent COVID-19 pandemic.

This research outlines the technological structure of the entire Japanese manufacturing and service industry using the patent information from research and development (R&D) activities to set R&D goals.

By analyzing the technological development capability of individual companies, the direction of the companies' R&D activities and current state of technological fusion between them can be understood. A group of companies participating in a particular product/service market must have the same technological development capabilities. As a result, the ratio of patent applications by a company to the total number of applications in a technical field will be similar across companies. This study uses the inter-company correlation coefficient of the ratio of patent applications by technical field as an index of technological development capability. A total of 167 major companies covering the major industries of Japan were analyzed. The analysis period was 15 years from 2004 to 2018, and the technical fields were rearranged to 42 fields with reference to the International Patent Classification (IPC)-Technology Concordance used by the World Intellectual Property Organization (WIPO). Considering the fluctuation in patent application opportunities, the number of patent applications was collected for at least three years for the analysis of patent applications by technical field, company and industry.

Examining the entire Japanese industry, the research found that chemicals, ceramics, non-ferrous metals and electrical/electronic equipment act as intermediaries between the respective groups and are linked to the transportation equipment, electrical/electronic equipment and information and communication services industries that are currently driving the Japanese economy. However, the technical connections between these groups are relatively loose. Over the last 15 years, the propagation structure of technical knowledge information has not changed. The progress of technological fusion remains within the scope of commerce and is conditioned by commerce.

Studies focusing on the technological development capability between companies and the technological structure of the Japanese manufacturing and service industries are almost non-existent since 2000 when Japan's economic growth slowed. The analytical methods presented in this research can be applied to individual companies to gain an understanding of technical positions of companies and can be useful for planning a technical environment, business or R&D strategy.

The author examines the impact these efficient factors have on factor model comparison tests in US returns using the Bayesian model scan approach of Chib et al. (2020), and Chib et al.(2022).

Ehsani and Linnainmaa (2022) show that time-series efficient investment factors in US stock returns span and earn 40% higher Sharpe ratios than the original factors.

The author shows that the optimal asset pricing model is an eight-factor model which contains efficient versions of the market factor, value factor (HML) and long-horizon behavioral factor (FIN). The findings show that efficient factors enhance the performance of US factor model performance. The top performing asset pricing model does not change in recent data.

The author is the only one to examine if the efficient factors developed by Ehsani and Linnainmaa (2022) have an impact on model comparison tests in US stock returns.

This paper aims to examine the dynamic conditional correlation (DCC) and hedging ratios between Dow Jones markets and oil, gold and bitcoin. Using daily data, including the COVID-19 pandemic and the Russia–Ukraine war. We employ the DCC-generalized autoregressive conditional heteroskedasticity (GARCH) and asymmetric DCC (ADCC)-GARCH models.

DCC-GARCH and ADCC-GARCH models.

The most of DCCs among market pairs are positive during COVID-19 period, implying the existence of volatility spillovers (Contagion-effects). This implies the lack of additional economic gains of diversification. So, COVID-19 represents a systematic risk that resists diversification. However, during the Russia–Ukraine war the DCCs are negative for most pairs that include Oil and Gold, implying investors may benefit from portfolio-diversification. Our hedging analysis carries significant implications for investors seeking higher returns while hedging their Dow Jones portfolios: keeping their portfolios unhedged is better than hedging them. This is because Islamic stocks have the ability to mitigate risks.

Our paper may make a valuable contribution to the existing literature by examining the hedging of financial assets, including both conventional and Islamic assets, during periods of stability and crisis, such as the COVID-19 pandemic and the Russia–Ukraine war.

Using Nigeria, as a point of reference, this study aims to explore the applicability of climatic variables as analytically valid factors for conceptual cost estimation. This is in view of the vastness and topographical alignment of Nigeria's landmass, which makes it a country of extreme climatic variability from north to south. As construction costs in Nigeria, similarly, tend to show a north-south alignment, the study's objective is to establish cost-estimating relationships (CERs) between the variability of climatic elements and the variance in construction cost, to arouse interest in the concept.

Deploying correlation analysis and multiple regression analysis, significant associations/relationships between meteorological variables and building cost for selected locations, following a North-South transect of the major climatic zones, are sought, to explain climate-induced construction cost variance. Validation of the regression model was carried out using variance analysis and the Mean Absolute Percentage Error of a different dataset.

Climatic indices of atmospheric moisture exhibited strong direct and partial correlations with construction costs, while sunshine hours and temperature were inversely correlated. The study further establishes statistically significant CERs between climatic variables and building cost in Nigeria, which accounted for 47.9% of the variance in construction cost across the climatic zones.

The study outcome provides a statistically valid platform for the development of more elaborate analytical costing models, for prototype buildings to be cited in disparate climatic settings.

This study establishes the statistical validity of climatic variables in constituting CERs for predicting construction costs in disparate climatic settings.

This paper examines the time-varying efficiency of nine major Middle East and North Africa (MENA) stock markets namely Egypt, Bahrain, UAE, Jordan, Saudi Arabia, Oman, Qatar, Morocco and Tunisia during times of COVID-19 pandemic outbreak and vaccines.

The authors use two econometric approaches: (1) autocorrelation tests including the wild bootstrap automatic variance ratio test, the automatic portmanteau test and the Generalized spectral test, and (2) a non-Bayesian generalized least squares-based time-varying model with statistical inferences.

The results show that the degree of stock market efficiency of Egyptian, Bahraini, Saudi, Moroccan and Tunisian stock markets is influenced by the COVID-19 pandemic crisis. Furthermore, the authors find a tendency toward efficiency in most of the MENA markets after the announcement of the COVID-19's vaccine approval. Finally, the Jordanian, Omani, Qatari and UAE stock markets remain globally efficient during the three sub-periods of the COVID-19 pandemic outbreak.

The results have important implications for asset allocations and financial risk management. Portfolio managers may maximize the benefit of arbitrage opportunities by taking strategic long and short positions in these markets during downward trend periods. Policymakers should implement the action plans and reforms to protect the stock markets from global shocks and ensure the stability of the stock markets.